Time is of essence in the medical field, where a patient’s situation might change drastically in a matter of days. In light of the extremely time-sensitive nature of the field, any method to reduce delays needs to be analyzed thoroughly and implemented wherever possible. Artificial Intelligence coupled with large scale data has enabled the proliferation of technology in the medical domain [1]}, rapidly replacing human effort with nearly the same or even increased accuracy. Various benchmarks have been established for image segmentation [2]}, [3]}, object detection [4]}, and identification [5]}; and new and improved deep learning techniques have been found to consistently perform better than humans at such tasks [6]}. This is because the architecture of neural networks makes it easy for them to downsample the images, thereby extracting localized features, which can then be combined to effect highly accurate results [7]}, in a fraction of the time a normal human would take.
Another factor apart from time is cost, and in a field already stretched thin in terms of resources, even marginal errors might lead to a massive increase in costs for patients. This also discourages a vast section of the people from seeking medical advice in the first place, owing to the time-consuming and expensive nature of the process. A reduction in processing time means a reduction in cost for patients too, since one system can efficiently perform the task of multiple technicians, with its accuracy increasing with the number of images it processes, utilizing the concept of backpropagation [8]}.
It is thus logical to infer that efforts need to be made to optimize the diagnostic pipeline, and state-of-the-art techniques developed in this regard would have far-reaching effects on the general well-being of the society. In order to proceed in this direction, this paper proposes a UNet architecture [9]} for lung segmentation from chest X-ray images, thus shortening the otherwise manual process, with a considerable spike in accuracy over other image processing approaches.
